We present a novel algorithm for clustering streams of multidimensional points based on kernel density estimates of the data. The algorithm requires only one pass over each data point and a constant amount of space, which depends only on the accuracy of clustering. The algorithm recognizes clusters of nonspherical shapes and handles both inserted and deleted objects in the input stream. Querying the membership of a point in a cluster can be answered in constant time.

S. Lodi, G. Moro, C. Sartori (2006). Stream Clustering Based on Kernel Density Estimation. AMSTERDAM : IOS Press.

Stream Clustering Based on Kernel Density Estimation

LODI, STEFANO;MORO, GIANLUCA;SARTORI, CLAUDIO
2006

Abstract

We present a novel algorithm for clustering streams of multidimensional points based on kernel density estimates of the data. The algorithm requires only one pass over each data point and a constant amount of space, which depends only on the accuracy of clustering. The algorithm recognizes clusters of nonspherical shapes and handles both inserted and deleted objects in the input stream. Querying the membership of a point in a cluster can be answered in constant time.
2006
ECAI 2006 17th European Conference on Artificial Intelligence
799
800
S. Lodi, G. Moro, C. Sartori (2006). Stream Clustering Based on Kernel Density Estimation. AMSTERDAM : IOS Press.
S. Lodi; G. Moro; C. Sartori
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/29744
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